Research

In this project, a new data-driven self-learning optimal control for complex industrial nonlinear systems will be studied. According to the difficulties of mathematical modeling for the current complex industrial systems, a new data-driven adaptive dynamic programming based self-learning optimal control theory and method will be established, which openes up a new research area for controls and optimaztions of complex industrial nonlinear systems. The main research contents include: 1. Develop a new data-driven value iterative adaptive dynamic programming of complex nonlinear systems and analyse convergnce and stability properties. 2. Develop a new data-driven policy iterative adaptive dynamic programming of complex nonlinear systems and analyse the convergnce and stability properties. 3. Establish a new stable data-driven iterative adaptive dynamic programming theory, which overcomes the disadvantages of value and policy iterative adaptive dynamic programming methods, and develops the superiorities of the algorithm. 4. Construct a new error-based data-driven iterative adaptive dynamic programming method, which overcomes the inaccuracy of the systems by the data-driven methods, and obtains the optimal control of the system. 5. Verify the corrections of the research results by simulations and then apply the research results to real control platform of industrial systems to create economic benefits. This project will also provide a new direction of optimal control theory for complex control system and push forward the frontier of China's automation techniques.